code: def flatten(g: jit_utils.GraphContext, input, start_dim, end_dim):
      start_dim_i = symbolic_helper._get_const(
          start_dim, "i", "start_dim"
      )
      end_dim_i = symbolic_helper._get_const(end_dim, "i", "end_dim")
  
      dim = input.type().dim()
      if end_dim_i < 0:
          end_dim_i = dim + end_dim_i
      # use ONNX's Flatten operator for cases where the output shape is 2D
      if start_dim_i == 1 and end_dim_i == dim - 1:
          if symbolic_helper._try_get_scalar_type(input):
              old_type, input = _try_cast_integer_to_float(g, input)
              return _cast_to_type(
                  g,
                  g.op("Flatten", input, axis_i=start_dim_i),
                  old_type,
              )
          else:
              return g.op("Flatten", input, axis_i=start_dim_i)
      if start_dim_i == 0 and end_dim_i == dim - 2:
          if symbolic_helper._try_get_scalar_type(input):
              old_type, input = _try_cast_integer_to_float(g, input)
              return _cast_to_type(
                  g,
                  g.op("Flatten", input, axis_i=end_dim_i + 1),
                  old_type,
              )
          else:
              return g.op("Flatten", input, axis_i=end_dim_i + 1)
  
      return opset9.flatten(g, input, start_dim, end_dim)
  
imports: import functools
  import warnings
  import torch
  from torch._C import _onnx
  from torch.onnx import _type_utils, errors, symbolic_helper, symbolic_opset9
  from torch.onnx._internal import jit_utils, registration
label_desc: Use image node_id 14 for calling a global function with example usage: flatten(g, input, start_dim, end_dim) and returns: opset9, _cast_to_type, g, _cast_to_type, g